Evidence map›Paper›PMID 34906747›Full record

Trial reportContemporary clinical trials2022

Multiplicity adjustments in parallel-group multi-arm trials sharing a control group: Clear guidance is needed.

Síle F Molloy, Ian R White, Andrew J Nunn, Richard Hayes, Duolao Wang, Thomas S Harrison

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Contemporary clinical trials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Trial
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  10. An Epidemiologic Approach for Estimating Risk Reduction and Asymptotic Power on the Log-Difference Scale.International journal of environmental research and public health · 2025
    Article
  11. Article
  12. Article
  13. Observational
  14. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Síle F MolloyInstitute for Infection and Immunity, St George's University of London, London, UK. Electronic address: smolloy@sgul.ac.uk.
Ian R WhiteMRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK.
Andrew J NunnMRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK.
Richard HayesDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
Duolao WangGlobal Health Trials Unit, Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK.
Thomas S HarrisonInstitute for Infection and Immunity, St George's University of London, London, UK.

Funding

Medical Research Council MC_UU_00004/04Medical Research Council MC_UU_00004/07Medical Research Council MC_UU_00004/09Medical Research Council MR/R010161/1
6 · The paper itself

Abstract

Multi-arm, parallel-group clinical trials are an efficient way of testing several new treatments, treatment regimens or doses. However, guidance on the requirement for statistical adjustment to control for multiple comparisons (type I error) using a shared control group is unclear. We argue, based on current evidence, that adjustment is not always necessary in such situations. We propose that adjustment should not be a requirement in multi-arm, parallel-group trials testing distinct treatments and sharing a control group, and we call for clearer guidance from stakeholders, such as regulators and scientific journals, on the appropriate settings for adjustment of multiplicity.

Indexed as

Research DesignControl GroupsHumansFamily-wise error rate (FWER)Multi-arm, parallel-group clinical trialsMultiplicityType 1 error

Identifiers

PMID34906747
PMCPMC8844584

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.